Articles | Volume 17, issue 5
https://doi.org/10.5194/gmd-17-2053-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-2053-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
12 Mar 2024
Development and technical paper |
| 12 Mar 2024
| 12 Mar 2024
PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations
National Research Council of Italy, Institute of Methodologies for Environmental Analysis (CNR-IMAA), Tito Scalo (Potenza), 85050, Italy
National Research Council of Italy, Institute of Methodologies for Environmental Analysis (CNR-IMAA), Tito Scalo (Potenza), 85050, Italy
CETEMPS, University of L'Aquila, L'Aquila, 67100, Italy
Donatello Gallucci
National Research Council of Italy, Institute of Methodologies for Environmental Analysis (CNR-IMAA), Tito Scalo (Potenza), 85050, Italy
Saverio Teodosio Nilo
National Research Council of Italy, Institute of Methodologies for Environmental Analysis (CNR-IMAA), Tito Scalo (Potenza), 85050, Italy
Filomena Romano
National Research Council of Italy, Institute of Methodologies for Environmental Analysis (CNR-IMAA), Tito Scalo (Potenza), 85050, Italy
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EGUsphere, https://doi.org/10.5194/egusphere-2024-3906, https://doi.org/10.5194/egusphere-2024-3906, 2025
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We assess and illustrate the benefits of high-altitude attainment of balloon-borne radiosonde soundings up to and beyond 10 hPa level from various aspects. We show that the extra costs and technical challenges involved in consistent attainment of high ascents are more than outweighed by the benefits for a broad variety of real time and delayed mode applications. Consistent attainment of high ascents should therefore be pursued across the balloon observational network.
Donatello Gallucci, Domenico Cimini, Emma Turner, Stuart Fox, Philip W. Rosenkranz, Mikhail Y. Tretyakov, Vinia Mattioli, Salvatore Larosa, and Filomena Romano
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Nowadays, atmospheric radiative transfer models are widely used to simulate satellite and ground-based observations. A meaningful comparison between observations and simulations requires an estimate of the uncertainty associated with both. This work quantifies the uncertainty in atmospheric radiative transfer models in the microwave range, providing the uncertainty associated with simulations of new-generation satellite microwave sensors.
Domenico Cimini, James Hocking, Francesco De Angelis, Angela Cersosimo, Francesco Di Paola, Donatello Gallucci, Sabrina Gentile, Edoardo Geraldi, Salvatore Larosa, Saverio Nilo, Filomena Romano, Elisabetta Ricciardelli, Ermann Ripepi, Mariassunta Viggiano, Lorenzo Luini, Carlo Riva, Frank S. Marzano, Pauline Martinet, Yun Young Song, Myoung Hwan Ahn, and Philip W. Rosenkranz
Geosci. Model Dev., 12, 1833–1845, https://doi.org/10.5194/gmd-12-1833-2019, https://doi.org/10.5194/gmd-12-1833-2019, 2019
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The fast radiative transfer model RTTOV-gb was developed to foster ground-based microwave radiometer data assimilation into numerical weather prediction models, as introduced in a companion paper (https://doi.org/10.5194/gmd-9-2721-2016). Here we present the updates and new features of the current version (v1.0), which is freely accessible online.
Domenico Cimini, Rémi Gandoin, Stephanie Fiedler, Claudia Acquistapace, Andrea Balotti, Sabrina Gentile, Edoardo Geraldi, Christine Knist, Pauline Martinet, Saverio T. Nilo, Giandomenico Pace, Bernhard Pospichal, and Filomena Romano
Atmos. Meas. Tech., 18, 2041–2067, https://doi.org/10.5194/amt-18-2041-2025, https://doi.org/10.5194/amt-18-2041-2025, 2025
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Atmospheric stability indicates whether air vertical motion is dumped or amplified. This is important for wind energy applications as it affects wind turbine wakes and thus the yield of wind parks. The paper provides an assessment of stability metrics measured by ground-based microwave radiometers in different climatological conditions and instrument types, onshore and offshore. Results indicate that special precaution may be required offshore to achieve typical onshore performances.
Masatomo Fujiwara, Bomin Sun, Anthony Reale, Domenico Cimini, Salvatore Larosa, Lori Borg, Christoph von Rohden, Michael Sommer, Ruud Dirksen, Marion Maturilli, Holger Vömel, Rigel Kivi, Bruce Ingleby, Ryan J. Kramer, Belay Demoz, Fabio Madonna, Fabien Carminati, Owen Lewis, Brett Candy, Christopher Thomas, David Edwards, Noersomadi, Kensaku Shimizu, and Peter Thorne
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We assess and illustrate the benefits of high-altitude attainment of balloon-borne radiosonde soundings up to and beyond 10 hPa level from various aspects. We show that the extra costs and technical challenges involved in consistent attainment of high ascents are more than outweighed by the benefits for a broad variety of real time and delayed mode applications. Consistent attainment of high ascents should therefore be pursued across the balloon observational network.
Fabio Madonna, Benedetto De Rosa, Simone Gagliardi, Ilaria Gandolfi, Yassmina Hesham Essa, Domenico Madonna, Fabrizio Marra, Maria Assunta Menniti, Donato Summa, Emanuele Tramutola, Faezeh Karimian Saracks, Filomena Romano, and Marco Rosoldi
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Revised manuscript not accepted
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Extreme weather events in the Mediterranean have intensified, and understanding their causes is key to improving forecasts. This study used data from the MESSA-DIN measurement campaign in Soverato, Italy (July–September 2021) to analyze the water vapor fluxes effect in extreme weather events as well as the related ERA5 performances. This study highlights a bias in ERA5 reanalysis humidity and the importance of high-resolution data for improving weather predictions.
Stuart Fox, Vinia Mattioli, Emma Turner, Alan Vance, Domenico Cimini, and Donatello Gallucci
Atmos. Meas. Tech., 17, 4957–4978, https://doi.org/10.5194/amt-17-4957-2024, https://doi.org/10.5194/amt-17-4957-2024, 2024
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Airborne observations are used to evaluate two models for absorption and emission by atmospheric gases, including water vapour and oxygen, at microwave and sub-millimetre wavelengths. These models are needed for the Ice Cloud Imager (ICI) on the next generation of European polar-orbiting weather satellites, which measures at frequencies up to 664 GHz. Both models can provide a good match to measurements from airborne radiometers and are sufficiently accurate for use with ICI.
Donatello Gallucci, Domenico Cimini, Emma Turner, Stuart Fox, Philip W. Rosenkranz, Mikhail Y. Tretyakov, Vinia Mattioli, Salvatore Larosa, and Filomena Romano
Atmos. Chem. Phys., 24, 7283–7308, https://doi.org/10.5194/acp-24-7283-2024, https://doi.org/10.5194/acp-24-7283-2024, 2024
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Nowadays, atmospheric radiative transfer models are widely used to simulate satellite and ground-based observations. A meaningful comparison between observations and simulations requires an estimate of the uncertainty associated with both. This work quantifies the uncertainty in atmospheric radiative transfer models in the microwave range, providing the uncertainty associated with simulations of new-generation satellite microwave sensors.
Simone Kotthaus, Juan Antonio Bravo-Aranda, Martine Collaud Coen, Juan Luis Guerrero-Rascado, Maria João Costa, Domenico Cimini, Ewan J. O'Connor, Maxime Hervo, Lucas Alados-Arboledas, María Jiménez-Portaz, Lucia Mona, Dominique Ruffieux, Anthony Illingworth, and Martial Haeffelin
Atmos. Meas. Tech., 16, 433–479, https://doi.org/10.5194/amt-16-433-2023, https://doi.org/10.5194/amt-16-433-2023, 2023
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Profile observations of the atmospheric boundary layer now allow for layer heights and characteristics to be derived at high temporal and vertical resolution. With novel high-density ground-based remote-sensing measurement networks emerging, horizontal information content is also increasing. This review summarises the capabilities and limitations of various sensors and retrieval algorithms which need to be considered during the harmonisation of data products for high-impact applications.
Ayham Alyosef, Domenico Cimini, Lorenzo Luini, Carlo Riva, Frank S. Marzano, Marianna Biscarini, Luca Milani, Antonio Martellucci, Sabrina Gentile, Saverio T. Nilo, Francesco Di Paola, Ayman Alkhateeb, and Filomena Romano
Atmos. Meas. Tech., 14, 2737–2748, https://doi.org/10.5194/amt-14-2737-2021, https://doi.org/10.5194/amt-14-2737-2021, 2021
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Telecommunication is based on the propagation of radio signals through the atmosphere. The signal power diminishes along the path due to atmospheric attenuation, which needs to be estimated to be accounted for. In a study funded by the European Space Agency, we demonstrate an innovative method improving atmospheric attenuation estimates from ground-based radiometric measurements by 10–30 %. More accurate atmospheric attenuation estimates imply better telecommunication services in the future.
Pauline Martinet, Domenico Cimini, Frédéric Burnet, Benjamin Ménétrier, Yann Michel, and Vinciane Unger
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Domenico Cimini, James Hocking, Francesco De Angelis, Angela Cersosimo, Francesco Di Paola, Donatello Gallucci, Sabrina Gentile, Edoardo Geraldi, Salvatore Larosa, Saverio Nilo, Filomena Romano, Elisabetta Ricciardelli, Ermann Ripepi, Mariassunta Viggiano, Lorenzo Luini, Carlo Riva, Frank S. Marzano, Pauline Martinet, Yun Young Song, Myoung Hwan Ahn, and Philip W. Rosenkranz
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The fast radiative transfer model RTTOV-gb was developed to foster ground-based microwave radiometer data assimilation into numerical weather prediction models, as introduced in a companion paper (https://doi.org/10.5194/gmd-9-2721-2016). Here we present the updates and new features of the current version (v1.0), which is freely accessible online.
Domenico Cimini, Philip W. Rosenkranz, Mikhail Y. Tretyakov, Maksim A. Koshelev, and Filomena Romano
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The paper presents a general approach to quantify the uncertainty related to atmospheric absorption models. These models describe how the atmosphere interacts with radiation, and they have general implications for atmospheric sciences.
The presented approach contributes to a better understanding of the total uncertainty affecting atmospheric radiative properties, thus reducing the chances of systematic errors when observations are exploited for weather forecast or climate trend derivations.
Francesco De Angelis, Domenico Cimini, Ulrich Löhnert, Olivier Caumont, Alexander Haefele, Bernhard Pospichal, Pauline Martinet, Francisco Navas-Guzmán, Henk Klein-Baltink, Jean-Charles Dupont, and James Hocking
Atmos. Meas. Tech., 10, 3947–3961, https://doi.org/10.5194/amt-10-3947-2017, https://doi.org/10.5194/amt-10-3947-2017, 2017
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Modern data assimilation systems require knowledge of the typical differences between observations and model background (O–B). This work illustrates a 1-year O–B analysis for ground-based microwave radiometer (MWR) observations in clear-sky conditions for a prototype network of six MWRs in Europe. Observations are MWR brightness temperatures (TB). Background profiles extracted from the output of a convective-scale model are used to simulate TB through the radiative transfer model RTTOV-gb.
Pauline Martinet, Domenico Cimini, Francesco De Angelis, Guylaine Canut, Vinciane Unger, Remi Guillot, Diane Tzanos, and Alexandre Paci
Atmos. Meas. Tech., 10, 3385–3402, https://doi.org/10.5194/amt-10-3385-2017, https://doi.org/10.5194/amt-10-3385-2017, 2017
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Microwave radiometers have the capability of observing temperature and humidity profiles with a few minute time resolution. This study investigates the potential benefit of this instrument to improve weather forecasts thanks to a better initialization of the model. Our results show that a significant improvement can be expected in the model initialization in the first 3 km with potential impacts on weather forecasts.
Francesco De Angelis, Domenico Cimini, James Hocking, Pauline Martinet, and Stefan Kneifel
Geosci. Model Dev., 9, 2721–2739, https://doi.org/10.5194/gmd-9-2721-2016, https://doi.org/10.5194/gmd-9-2721-2016, 2016
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Ground-based microwave radiometers (MWRs) offer to bridge the observational gap in the atmospheric boundary layer. Currently MWRs are operational at many sites worldwide. However, their potential is largely under-exploited, partly due to the lack of a fast radiative transfer model (RTM) suited for data assimilation into numerical weather prediction models. Here we propose and test an RTM, building on satellite heritage and adapting for ground-based MWRs, which addresses this shortage.
D. Cimini, M. Nelson, J. Güldner, and R. Ware
Atmos. Meas. Tech., 8, 315–333, https://doi.org/10.5194/amt-8-315-2015, https://doi.org/10.5194/amt-8-315-2015, 2015
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Forecast indices commonly used in operational meteorology can be computed from temperature and humidity profiles retrieved from a ground-based microwave radiometer.
The values of radiometer-derived forecast indices agree well with values computed from radiosondes (correlation usually above 0.8).
Radiometer-derived forecast indices offer the advantage (with respect to radiosondes) of nearly continuous data, capturing the entire diurnal cycle and providing fresh and timely data to forecasters.
E. Ricciardelli, D. Cimini, F. Di Paola, F. Romano, and M. Viggiano
Hydrol. Earth Syst. Sci., 18, 2559–2576, https://doi.org/10.5194/hess-18-2559-2014, https://doi.org/10.5194/hess-18-2559-2014, 2014
D. Cimini, F. Romano, E. Ricciardelli, F. Di Paola, M. Viggiano, F. S. Marzano, V. Colaiuda, E. Picciotti, G. Vulpiani, and V. Cuomo
Atmos. Meas. Tech., 6, 3181–3196, https://doi.org/10.5194/amt-6-3181-2013, https://doi.org/10.5194/amt-6-3181-2013, 2013
D. Cimini, F. De Angelis, J.-C. Dupont, S. Pal, and M. Haeffelin
Atmos. Meas. Tech., 6, 2941–2951, https://doi.org/10.5194/amt-6-2941-2013, https://doi.org/10.5194/amt-6-2941-2013, 2013
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The Meso-NH weather research code is adapted for GPUs using OpenACC, leading to significant performance and energy efficiency improvements. Called MESONH-v55-OpenACC, it includes enhanced memory management, communication optimizations and a new solver. On the AMD MI250X Adastra platform, it achieved up to 6× speedup and 2.3× energy efficiency gain compared to CPUs. Storm simulations at 100 m resolution show positive results, positioning the code for future use on exascale supercomputers.
Jie Gao, Yi Huang, Jonathon S. Wright, Ke Li, Tao Geng, and Qiurun Yu
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The aerosol in the upper troposphere and stratosphere is highly variable, and its radiative effect is poorly understood. To estimate this effect, the radiative kernel is constructed and applied. The results show that the kernels can reproduce aerosol radiative effects and are expected to simulate stratospheric aerosol radiative effects. This approach reduces computational expense, is consistent with radiative model calculations, and can be applied to atmospheric models with speed requirements.
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev., 18, 2303–2328, https://doi.org/10.5194/gmd-18-2303-2025, https://doi.org/10.5194/gmd-18-2303-2025, 2025
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Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
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Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
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To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify, and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known
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Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025, https://doi.org/10.5194/gmd-18-1965-2025, 2025
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The MAR (Modèle Régional Atmosphérique) is a regional climate model used for weather forecasting and studying the climate over various regions. This paper presents an update of MAR thanks to which it can precisely decompose solar radiation, in particular in the UV (ultraviolet) and photosynthesis ranges, both being critical to human health and ecosystems. As a first application of this new capability, this paper presents a method for predicting UV indices with MAR.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025, https://doi.org/10.5194/gmd-18-1947-2025, 2025
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Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds significant potential for improving forecast accuracy. Radiative transfer models (RTMs) play a crucial role in direct data assimilation. In this study, we introduce a new RTM, the Advanced Radiative Transfer Modeling System – Ground-Based (ARMS-gb), designed to simulate brightness temperatures observed by GMRs along with their Jacobians. Several enhancements have been incorporated to achieve higher accuracy.
R. Phani Murali Krishna, Siddharth Kumar, A. Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, Sahadat Sarkar, Medha Deshpande, and Parthasarathi Mukhopadhyay
Geosci. Model Dev., 18, 1879–1894, https://doi.org/10.5194/gmd-18-1879-2025, https://doi.org/10.5194/gmd-18-1879-2025, 2025
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The High-Resolution Global Forecast Model (HGFM) is an advanced iteration of the operational Global Forecast System (GFS) model. HGFM can produce forecasts at a spatial scale of ~6 km in tropics. It demonstrates improved accuracy in short- to medium-range weather prediction over the Indian region, with notable success in predicting extreme events. Further, the model will be entrusted to operational forecasting agencies after validation and testing.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
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Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev., 18, 1809–1827, https://doi.org/10.5194/gmd-18-1809-2025, https://doi.org/10.5194/gmd-18-1809-2025, 2025
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Particles in the Earth's atmosphere strongly impact the planet's energy budget, and atmosphere simulations require accurate representation of their interaction with light. This work introduces two approaches to represent light scattering by small particles. The first is a scattering simulator based on Mie theory implemented in Python. The second is a neural network emulator that is more accurate than existing methods and is fast enough to be used in climate and weather simulations.
Qin Wang, Bo Zeng, Gong Chen, and Yaoting Li
Geosci. Model Dev., 18, 1769–1784, https://doi.org/10.5194/gmd-18-1769-2025, https://doi.org/10.5194/gmd-18-1769-2025, 2025
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This study evaluates the performance of four planetary boundary layer (PBL) schemes in near-surface wind fields over the Sichuan Basin, China. Using 112 sensitivity experiments with the Weather Research and Forecasting (WRF) model and focusing on 28 wind events, it is found that wind direction was less sensitive to the PBL schemes. The quasi-normal scale elimination (QNSE) scheme captured temporal variations best, while the Mellor–Yamada–Janjić (MYJ) scheme had the least error in wind speed.
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025, https://doi.org/10.5194/gmd-18-1661-2025, 2025
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It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Wei Li, Beiming Tang, Patrick C. Campbell, Youhua Tang, Barry Baker, Zachary Moon, Daniel Tong, Jianping Huang, Kai Wang, Ivanka Stajner, and Raffaele Montuoro
Geosci. Model Dev., 18, 1635–1660, https://doi.org/10.5194/gmd-18-1635-2025, https://doi.org/10.5194/gmd-18-1635-2025, 2025
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The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during the summer of 2023 shows that the updated model overall improves the simulation of MDA8 O3 by reducing the bias by 8%–12% in the contiguous US. PM2.5 predictions have mixed results due to wildfire, highlighting the need for future refinements.
Yanwei Zhu, Aitor Atencia, Markus Dabernig, and Yong Wang
Geosci. Model Dev., 18, 1545–1559, https://doi.org/10.5194/gmd-18-1545-2025, https://doi.org/10.5194/gmd-18-1545-2025, 2025
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Most works have delved into convective weather nowcasting, and only a few works have discussed the nowcasting uncertainty for variables at the surface level. Hence, we proposed a method to estimate uncertainty. Generating appropriate noises associated with the characteristic of the error in analysis can simulate the uncertainty of nowcasting. This method can contribute to the estimation of near–surface analysis uncertainty in both nowcasting applications and ensemble nowcasting development.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
Geosci. Model Dev., 18, 1505–1544, https://doi.org/10.5194/gmd-18-1505-2025, https://doi.org/10.5194/gmd-18-1505-2025, 2025
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The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a new implementation of the ensemble mode, building upon the initial developments.
Astrid Kerkweg, Timo Kirfel, Duong H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev., 18, 1265–1286, https://doi.org/10.5194/gmd-18-1265-2025, https://doi.org/10.5194/gmd-18-1265-2025, 2025
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Normally, the Modular Earth Submodel System (MESSy) is linked to complete dynamic models to create chemical climate models. However, the modular concept of MESSy and the newly developed DWARF component presented here make it possible to create simplified models that contain only one or a few process descriptions. This is very useful for technical optimisation, such as porting to GPUs, and can be used to create less complex models, such as a chemical box model.
Edward C. Chan, Ilona J. Jäkel, Basit Khan, Martijn Schaap, Timothy M. Butler, Renate Forkel, and Sabine Banzhaf
Geosci. Model Dev., 18, 1119–1139, https://doi.org/10.5194/gmd-18-1119-2025, https://doi.org/10.5194/gmd-18-1119-2025, 2025
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An enhanced emission module has been developed for the PALM model system, improving flexibility and scalability of emission source representation across different sectors. A model for parametrized domestic emissions has also been included, for which an idealized model run is conducted for particulate matter (PM10). The results show that, in addition to individual sources and diurnal variations in energy consumption, vertical transport and urban topology play a role in concentration distribution.
Gregor Ehrensperger, Thorsten Simon, Georg J. Mayr, and Tobias Hell
Geosci. Model Dev., 18, 1141–1153, https://doi.org/10.5194/gmd-18-1141-2025, https://doi.org/10.5194/gmd-18-1141-2025, 2025
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As lightning is a brief and localized event, it is not explicitly resolved in atmospheric models. Instead, expert-based auxiliary descriptions are used to assess it. This study explores how AI can improve our understanding of lightning without relying on traditional expert knowledge. We reveal that AI independently identified the key factors known to experts as essential for lightning in the Alps region. This shows how knowledge discovery could be sped up in areas with limited expert knowledge.
David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev., 18, 1103–1118, https://doi.org/10.5194/gmd-18-1103-2025, https://doi.org/10.5194/gmd-18-1103-2025, 2025
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The effect of the assumed atmospheric nucleation mechanism on particle number concentrations and size distribution was investigated. Two quite different mechanisms involving sulfuric acid and ammonia or a biogenic organic vapor gave quite similar results which were consistent with measurements at 26 measurement stations across Europe. The number of larger particles that serve as cloud condensation nuclei showed little sensitivity to the assumed nucleation mechanism.
Tim Radke, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus
Geosci. Model Dev., 18, 1017–1039, https://doi.org/10.5194/gmd-18-1017-2025, https://doi.org/10.5194/gmd-18-1017-2025, 2025
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In our study, we built upon previous work to investigate the patterns artificial intelligence (AI) learns to detect atmospheric features like tropical cyclones (TCs) and atmospheric rivers (ARs). As primary objective, we adopt a method to explain the AI used and investigate the plausibility of learned patterns. We find that plausible patterns are learned for both TCs and ARs. Hence, the chosen method is very useful for gaining confidence in the AI-based detection of atmospheric features.
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
EGUsphere, https://doi.org/10.5194/egusphere-2024-3512, https://doi.org/10.5194/egusphere-2024-3512, 2025
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Non-thermal emission from chemical reactions in the Earth's middle und upper atmosphere strongly contributes to the brightness of the night sky below about 2.3 µm. The new Paranal Airglow Line and Continuum Emission model calculates the emission spectrum and its variability with an unprecedented accuracy. Relying on a large spectroscopic data set from astronomical spectrographs and theoretical molecular/atomic data, it is valuable for airglow research and astronomical observatories.
Felipe Cifuentes, Henk Eskes, Enrico Dammers, Charlotte Bryan, and Folkert Boersma
Geosci. Model Dev., 18, 621–649, https://doi.org/10.5194/gmd-18-621-2025, https://doi.org/10.5194/gmd-18-621-2025, 2025
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We tested the capability of the flux divergence approach (FDA) to reproduce known NOx emissions using synthetic NO2 satellite column retrievals from high-resolution model simulations. The FDA accurately reproduced NOx emissions when column observations were limited to the boundary layer and when the variability of the NO2 lifetime, the NOx : NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces strong model dependency, reducing the simplicity of the original FDA formulation.
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025, https://doi.org/10.5194/gmd-18-529-2025, 2025
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We explore a high-level programming model for porting numerical weather prediction (NWP) model codes to graphics processing units (GPUs). We present a Python rewrite with the domain-specific library GT4Py (GridTools for Python) of two renowned cloud microphysics schemes and the associated tangent-linear and adjoint algorithms. We find excellent portability, competitive GPU performance, robust execution on diverse computing architectures, and enhanced code maintainability and user productivity.
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025, https://doi.org/10.5194/gmd-18-483-2025, 2025
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Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025, https://doi.org/10.5194/gmd-18-433-2025, 2025
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This paper presents the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a computationally efficient tool that uses machine learning algorithms for sensitivity analysis in atmospheric models. It is tested with the Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to investigate sea breeze circulation sensitivity to vegetation-related parameters.
Robert Schoetter, Robin James Hogan, Cyril Caliot, and Valéry Masson
Geosci. Model Dev., 18, 405–431, https://doi.org/10.5194/gmd-18-405-2025, https://doi.org/10.5194/gmd-18-405-2025, 2025
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Radiation is relevant to the atmospheric impact on people and infrastructure in cities as it can influence the urban heat island, building energy consumption, and human thermal comfort. A new urban radiation model, assuming a more realistic form of urban morphology, is coupled to the urban climate model Town Energy Balance (TEB). The new TEB is evaluated with a reference radiation model for a variety of urban morphologies, and an improvement in the simulated radiative observables is found.
Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro
Geosci. Model Dev., 18, 253–286, https://doi.org/10.5194/gmd-18-253-2025, https://doi.org/10.5194/gmd-18-253-2025, 2025
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Contrails forming in some atmospheric conditions may persist and become strongly warming cirrus, while in other conditions may be neutral or cooling. We develop a contrail forecast model to predict contrail climate forcing for any arbitrary point in space and time and explore integration into flight planning and air traffic management. This approach enables contrail interventions to target high-probability high-climate-impact regions and reduce unintended consequences of contrail management.
Nils Eingrüber, Alina Domm, Wolfgang Korres, and Karl Schneider
Geosci. Model Dev., 18, 141–160, https://doi.org/10.5194/gmd-18-141-2025, https://doi.org/10.5194/gmd-18-141-2025, 2025
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Climate change adaptation measures like unsealings can reduce urban heat stress. As grass grid pavers have never been parameterized for microclimate model simulations with ENVI-met, a new parameterization was developed based on field measurements. To analyse the cooling potential, scenario analyses were performed for a densely developed area in Cologne. Statistically significant average cooling effects of up to −11.1 K were found for surface temperature and up to −2.9 K for 1 m air temperature.
Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025, https://doi.org/10.5194/gmd-18-117-2025, 2025
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The Artificial-Intelligence-based Nonspherical Aerosol Optical Scheme (AI-NAOS) was developed to improve the estimation of the aerosol direct radiation effect and was coupled online with a chemical weather model. The AI-NAOS scheme considers black carbon as fractal aggregates and soil dust as super-spheroids, encapsulated with hygroscopic aerosols. Real-case simulations emphasize the necessity of accurately representing nonspherical and inhomogeneous aerosols in chemical weather models.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev., 18, 101–115, https://doi.org/10.5194/gmd-18-101-2025, https://doi.org/10.5194/gmd-18-101-2025, 2025
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The Python tool Orbital-Radar transfers suborbital radar data (ground-based, airborne, and forward-simulated numerical weather prediction model) into synthetic spaceborne cloud profiling radar data, mimicking platform-specific instrument characteristics, e.g. EarthCARE or CloudSat. The tool's novelty lies in simulating characteristic errors and instrument noise. Thus, existing data sets are transferred into synthetic observations and can be used for satellite calibration–validation studies.
Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell
Geosci. Model Dev., 18, 1–18, https://doi.org/10.5194/gmd-18-1-2025, https://doi.org/10.5194/gmd-18-1-2025, 2025
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The Modular and Integrated Data Assimilation System (MIDAS) software is described. The flexible design of MIDAS enables both deterministic and ensemble prediction applications for the atmosphere and several other Earth system components. It is currently used for all main operational weather prediction systems in Canada and also for sea ice and sea surface temperature analysis. The use of MIDAS for multiple Earth system components will facilitate future research on coupled data assimilation.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, and Enrico Pisoni
EGUsphere, https://doi.org/10.5194/egusphere-2024-3690, https://doi.org/10.5194/egusphere-2024-3690, 2025
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We assess the relevance and utility indicators developed within FAIRMODE by evaluating 9 CAMS models in calculated air pollutant values. For NO2, the results highlight difficulties at traffic stations. For PM2.5 and PM10 the bias and Winter-Summer gradients reveal issues. O3 evaluation shows that e.g. seasonal gradients are useful. Overall, the indicators provide valuable insights into model limitations, yet there is a need to reconsider the strictness of some indicators for certain pollutants.
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024, https://doi.org/10.5194/gmd-17-8885-2024, 2024
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We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can reproduce PAH distribution well. The concentration of BaP (indicator species for PAHs) could exceed the target values of 1 ng m-3 over some areas (e.g., in central Europe, India, and eastern China). The change in BaP is lower than that in PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although the Action Plan has been implemented.
Marie Taufour, Jean-Pierre Pinty, Christelle Barthe, Benoît Vié, and Chien Wang
Geosci. Model Dev., 17, 8773–8798, https://doi.org/10.5194/gmd-17-8773-2024, https://doi.org/10.5194/gmd-17-8773-2024, 2024
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We have developed a complete two-moment version of the LIMA (Liquid Ice Multiple Aerosols) microphysics scheme. We have focused on collection processes, where the hydrometeor number transfer is often estimated in proportion to the mass transfer. The impact of these parameterizations on a convective system and the prospects for more realistic estimates of secondary parameters (reflectivity, hydrometeor size) are shown in a first test on an idealized case.
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024, https://doi.org/10.5194/gmd-17-8639-2024, 2024
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A new parameterisation for dynamic anthropogenic heat and electricity consumption is described. The model reproduced the temporal variation in and spatial distributions of electricity consumption and temperature well in summer and winter. The partial air conditioning was the most critical factor, significantly affecting the value of anthropogenic heat emission.
Arseniy Karagodin-Doyennel, Fredrik Jansson, Bart van Stratum, Hugo Denier van der Gon, Jordi Vilà-Guerau de Arellano, and Sander Houweling
EGUsphere, https://doi.org/10.5194/egusphere-2024-3721, https://doi.org/10.5194/egusphere-2024-3721, 2024
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We introduce a new simulation platform based on the Dutch Large-Eddy Simulation (DALES) to simulate carbon dioxide (CO2) emissions and their dispersion in the turbulent environments with hectometer resolution. This model incorporates both anthropogenic emission inventory and ecosystem exchanges. Simulation results for the main urban area in the Netherlands demonstrate a strong potential of DALES to enhance CO2 emission modeling, which is important for refining their reduction strategies.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
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To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, https://doi.org/10.5194/gmd-17-8373-2024, 2024
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Chemical transport model simulations are combined with ozone observations to estimate the bias in ozone attributable to US anthropogenic sources and individual sources of US background ozone: natural sources, non-US anthropogenic sources, and stratospheric ozone. Results indicate a positive bias correlated with US anthropogenic emissions during summer in the eastern US and a negative bias correlated with stratospheric ozone during spring.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
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Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
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The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Yongzhu Liu, Xiaoye Zhang, Wei Han, Chao Wang, Wenxing Jia, Deying Wang, Zhaorong Zhuang, and Xueshun Shen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-148, https://doi.org/10.5194/gmd-2024-148, 2024
Revised manuscript accepted for GMD
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In order to investigate the feedbacks of chemical data assimilation on meteorological forecasts, we developed a strongly coupled chemistry meteorology four-dimensional variational (4D-Var) assimilation system, CMA-GFS-AERO 4D-Var, based on the framework of the incremental analysis scheme of the CMA-GFS operational global numerical weather model. The results show that assimilating BC observations can generate analysis increments not only for BC but also for atmospheric variables.
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-201, https://doi.org/10.5194/gmd-2024-201, 2024
Revised manuscript accepted for GMD
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RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre and sub-km scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and improved representation of clouds and visibility.
Peter Kalverla, Imme Benedict, Chris Weijenborg, and Ruud J. van der Ent
EGUsphere, https://doi.org/10.5194/egusphere-2024-3401, https://doi.org/10.5194/egusphere-2024-3401, 2024
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We introduce a new version of WAM2layers, a computer program that tracks how the weather brings water from one place to another. It uses data from weather and climate models, whose resolution is steadily increasing. Processing the latest data became a challenge, and the updates presented here ensure that WAM2layers runs smoothly again. We also made it easier to use the program and to understand its source code. This makes it more transparent and reliable, and easier to maintain.
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024, https://doi.org/10.5194/gmd-17-8093-2024, 2024
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Inadequate representation of surface–atmosphere interaction processes is a major source of uncertainty in numerical weather prediction models. Here, an effort has been made to improve the Weather Research and Forecasting (WRF) model version 4.2.2 by introducing a unique theoretical framework under convective conditions. In addition, to enhance the potential applicability of the WRF modeling system, various commonly used similarity functions under convective conditions have also been installed.
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024, https://doi.org/10.5194/gmd-17-8069-2024, 2024
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Supercooled liquid clouds (liquid clouds colder than 0°C) are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We compare a single-column version of a weather model to observations with two different cloud schemes and find that both the dynamical environment and atmospheric aerosols are important for reproducing observations.
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024, https://doi.org/10.5194/gmd-17-7995-2024, 2024
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This study updates the CESM's aerosol schemes, focusing on dust, marine aerosol emissions, and secondary organic aerosol (SOA) . Dust emission modifications make deflation areas more continuous, improving results in North America and the sub-Arctic. Humidity correction to sea-salt emissions has a minor effect. Introducing marine organic aerosol emissions, coupled with ocean biogeochemical processes, and adding aqueous reactions for SOA formation advance the CESM's aerosol modelling results.
Cited articles
Anderson, G. P., Clough, S. A., Kneizys, F. X., Chetwynd, J. H., and Shettle, E. P.: AFGL atmospheric constituent profiles (0.120 km), unknown, 1986.
Ayala Pelaez, S. and Deline, C.: pySMARTS: SMARTS Python Wrapper (Simple Model of the Atmospheric Radiative Transfer of Sunshine), GitHub [code], https://doi.org/10.11578/DC.20210816.1, 2020.
Bauer, P., Geer, A. J., Lopez, P., and Salmond, D.: Direct 4D-Var assimilation of all-sky radiances. Part I: Implementation, Q. J. Roy. Meteor. Soc., 136, 1868–1885, https://doi.org/10.1002/qj.659, 2010.
Belikovich, M. V., Kulikov, M. Y., Makarov, D. S., Skalyga, N. K., Ryskin, V. G., Shvetsov, A. A., Krasil'nikov, A. A., Dementyeva, S. O., Serov, E. A., and Feigin, A. M.: Long-Term Observations of Microwave Brightness Temperatures over a Metropolitan Area: Comparison of Radiometric Data and Spectra Simulated with the Use of Radiosonde Measurements, Remote Sens.-Basel, 13, 2061, https://doi.org/10.3390/rs13112061, 2021.
Belikovich, M. V., Makarov, D. S., Serov, E. A., Kulikov, M. Y., and Feigin, A. M.: Validation of Atmospheric Absorption Models within the 20–60 GHz Band by Simultaneous Radiosonde and Microwave Observations: The Advantage of Using ECS Formalism, Remote Sens.-Basel, 14, 6042, https://doi.org/10.3390/rs14236042, 2022.
Berk, A., Conforti, P., Kennett, R., Perkins, T., Hawes, F., and Bosch, J. van den: MODTRAN6: a major upgrade of the MODTRAN radiative transfer code, in: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX, 13 June 2014, Baltimore, MD, United States, 113–119, https://doi.org/10.1117/12.2050433, 2014.
Bodeker, G. E., Bojinski, S., Cimini, D., Dirksen, R. J., Haeffelin, M., Hannigan, J. W., Hurst, D. F., Leblanc, T., Madonna, F., Maturilli, M., Mikalsen, A. C., Philipona, R., Reale, T., Seidel, D. J., Tan, D. G. H., Thorne, P. W., Vömel, H., and Wang, J.: Reference Upper-Air Observations for Climate: From Concept to Reality, B. Am. Meteor. Soc., 97, 123–135, https://doi.org/10.1175/BAMS-D-14-00072.1, 2016.
Boukabara, S.-A., Garrett, K., Grassotti, C., Iturbide-Sanchez, F., Chen, W., Jiang, Z., Clough, S. A., Zhan, X., Liang, P., Liu, Q., Islam, T., Zubko, V., and Mims, A.: A physical approach for a simultaneous retrieval of sounding, surface, hydrometeor, and cryospheric parameters from SNPP/ATMS, J. Geophys. Res.-Atmos., 118, 12600–12619, https://doi.org/10.1002/2013JD020448, 2013.
Buehler, S. A., Eriksson, P., Kuhn, T., von Engeln, A., and Verdes, C.: ARTS, the atmospheric radiative transfer simulator, J. Quant. Spectrosc. Ra., 91, 65–93, https://doi.org/10.1016/j.jqsrt.2004.05.051, 2005.
Buehler, S. A., Defer, E., Evans, F., Eliasson, S., Mendrok, J., Eriksson, P., Lee, C., Jiménez, C., Prigent, C., Crewell, S., Kasai, Y., Bennartz, R., and Gasiewski, A. J.: Observing ice clouds in the submillimeter spectral range: the CloudIce mission proposal for ESA's Earth Explorer 8, Atmos. Meas. Tech., 5, 1529–1549, https://doi.org/10.5194/amt-5-1529-2012, 2012.
Buehler, S. A., Mendrok, J., Eriksson, P., Perrin, A., Larsson, R., and Lemke, O.: ARTS, the Atmospheric Radiative Transfer Simulator – version 2.2, the planetary toolbox edition, Geosci. Model Dev., 11, 1537–1556, https://doi.org/10.5194/gmd-11-1537-2018, 2018.
Cadeddu, M. and Gibler, G.: Microwave Radiometer Profiler (MWRP), Atmospheric Radiation Measurement (ARM) program [data set], https://doi.org/10.5439/1025254, 2012.
Cadeddu, M. P. and Liljegren, J.: Microwave Radiometer Profiler (MWRP) Instrument Handbook, Atmospheric Radiation Measurement (ARM) program, https://doi.org/10.2172/1424403, 2018.
Cadeddu, M. P., Payne, V. H., Clough, S. A., Cady-Pereira, K., and Liljegren, J. C.: Effect of the Oxygen Line-Parameter Modeling on Temperature and Humidity Retrievals From Ground-Based Microwave Radiometers, IEEE T. Geosci. Remote, 45, 2216–2223, https://doi.org/10.1109/TGRS.2007.894063, 2007.
Cadeddu, M. P., Liljegren, J. C., and Turner, D. D.: The Atmospheric radiation measurement (ARM) program network of microwave radiometers: instrumentation, data, and retrievals, Atmos. Meas. Tech., 6, 2359–2372, https://doi.org/10.5194/amt-6-2359-2013, 2013.
Chapman, I. M., Naylor, D., Gom, B. G., Querel, R. R., and Davis-Imhof, P.: BTRAM: An Interactive Atmospheric Radiative Transfer Model, https://api.semanticscholar.org/CorpusID:201615669 (last access: 29 February 2023), 2010.
Cimini, D., Rosenkranz, P. W., Tretyakov, M. Y., Koshelev, M. A., and Romano, F.: Uncertainty of atmospheric microwave absorption model: impact on ground-based radiometer simulations and retrievals, Atmos. Chem. Phys., 18, 15231–15259, https://doi.org/10.5194/acp-18-15231-2018, 2018.
Cimini, D., Hocking, J., De Angelis, F., Cersosimo, A., Di Paola, F., Gallucci, D., Gentile, S., Geraldi, E., Larosa, S., Nilo, S., Romano, F., Ricciardelli, E., Ripepi, E., Viggiano, M., Luini, L., Riva, C., Marzano, F. S., Martinet, P., Song, Y. Y., Ahn, M. H., and Rosenkranz, P. W.: RTTOV-gb v1.0 – updates on sensors, absorption models, uncertainty, and availability, Geosci. Model Dev., 12, 1833–1845, https://doi.org/10.5194/gmd-12-1833-2019, 2019.
Cimini, D., Serio, C., Masiello, G., Mastro, P., Ricciardelli, E., Paola, F. D., Larosa, S., Gallucci, D., Hultberg, T., August, T., and Romano, F.: Spectrum Synergy for Investigating Cloud Microphysics, B. Am. Meteor. Soc., 104, E606–E622, https://doi.org/10.1175/BAMS-D-22-0008.1, 2023.
Clain, G., Brogniez, H., Payne, V. H., John, V. O., and Luo, M.: An Assessment of SAPHIR Calibration Using Quality Tropical Soundings, J. Atmos. Ocean. Tech., 32, 61–78, https://doi.org/10.1175/JTECH-D-14-00054.1, 2015.
Clough, S. A., Shephard, M. W., Mlawer, E. J., Delamere, J. S., Iacono, M. J., Cady-Pereira, K., Boukabara, S., and Brown, P. D.: Atmospheric radiative transfer modeling: a summary of the AER codes, J. Quant. Spectrosc. Ra., 91, 233–244, https://doi.org/10.1016/j.jqsrt.2004.05.058, 2005.
De Angelis, F., Cimini, D., Hocking, J., Martinet, P., and Kneifel, S.: RTTOV-gb – adapting the fast radiative transfer model RTTOV for the assimilation of ground-based microwave radiometer observations, Geosci. Model Dev., 9, 2721–2739, https://doi.org/10.5194/gmd-9-2721-2016, 2016.
De Angelis, F., Cimini, D., Löhnert, U., Caumont, O., Haefele, A., Pospichal, B., Martinet, P., Navas-Guzmán, F., Klein-Baltink, H., Dupont, J.-C., and Hocking, J.: Long-term observations minus background monitoring of ground-based brightness temperatures from a microwave radiometer network, Atmos. Meas. Tech., 10, 3947–3961, https://doi.org/10.5194/amt-10-3947-2017, 2017.
Ding, S., Yang, P., Weng, F., Liu, Q., Han, Y., van Delst, P., Li, J., and Baum, B.: Validation of the community radiative transfer model, J. Quant. Spectrosc. Ra., 112, 1050–1064, https://doi.org/10.1016/j.jqsrt.2010.11.009, 2011.
Durre, I., Yin, X., Vose, R. S., Applequist, S., Arnfield, J., Korzeniewski, B., and Hundermark, B.: Integrated Global Radiosonde Archive (IGRA), Version 2, NOAA National Centers for Environmental Information, https://doi.org/10.7289/V5X63K0Q, 2016.
Durre, I., Yin, X., Vose, R. S., Applequist, S., and Arnfield, J.: Enhancing the Data Coverage in the Integrated Global Radiosonde Archive, J. Atmos. Ocean. Tech., 35, 1753–1770, https://doi.org/10.1175/JTECH-D-17-0223.1, 2018.
Efremenko, D. S., Molina García, V., Pflug, B., and Trautmann, T.: PYDOME: python library for radiative transfer computations, in: Living Planet Symposium 2019, Milan, Italy, 13–17 May 2019, https://elib.dlr.de/128047/ (last access: 29 February 2024), 2019.
Eriksson, P., Jiménez, C., and Buehler, S. A.: Qpack, a general tool for instrument simulation and retrieval work, J. Quant. Spectrosc. Ra., 91, 47–64, https://doi.org/10.1016/j.jqsrt.2004.05.050, 2005.
Eriksson, P., Buehler, S. A., Davis, C. P., Emde, C., and Lemke, O.: ARTS, the atmospheric radiative transfer simulator, version 2, J. Quant. Spectrosc. Ra., 112, 1551–1558, https://doi.org/10.1016/j.jqsrt.2011.03.001, 2011.
ESA TN: TEC-EEP/2016.184/AM, Tropospheric Media Calibration System for accurate ranging of space science missions – Definitions of parameters and test procedures for assessment of instrument stability, ver 2.5, ESA ECEEP/2016.184/AM, November 2019.
Eyre, J. R., English, S. J., and Forsythe, M.: Assimilation of satellite data in numerical weather prediction. Part I: The early years, Q. J. Roy. Meteor. Soc., 146, 49–68, https://doi.org/10.1002/qj.3654, 2020.
Fox, S., Lee, C., Moyna, B., Philipp, M., Rule, I., Rogers, S., King, R., Oldfield, M., Rea, S., Henry, M., Wang, H., and Harlow, R. C.: ISMAR: an airborne submillimetre radiometer, Atmos. Meas. Tech., 10, 477–490, https://doi.org/10.5194/amt-10-477-2017, 2017.
Fox, S., Vance, A., Turner, E., Cimini, D., Gallucci, D., Romano, F., Prigent, C., and Pardo, J.: ISMAR study final report, EUMETSAT study report, 2023.
Gallucci, D., Cimini, D., Turner, E., Fox, S., Rosenkranz, P. W., Tretyakov, M. Y., Mattioli, V., Larosa, S., and Romano, F.: Uncertainty of simulated brightness temperature due to sensitivity to atmospheric gas spectroscopic parameters, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-3160, 2024.
Geer, A. J., Bauer, P., Lonitz, K., Barlakas, V., Eriksson, P., Mendrok, J., Doherty, A., Hocking, J., and Chambon, P.: Bulk hydrometeor optical properties for microwave and sub-millimetre radiative transfer in RTTOV-SCATT v13.0, Geosci. Model Dev., 14, 7497–7526, https://doi.org/10.5194/gmd-14-7497-2021, 2021.
Gordon, I. E., Rothman, L. S., Hill, C., Kochanov, R. V., Tan, Y., Bernath, P. F., Birk, M., Boudon, V., Campargue, A., Chance, K. V., Drouin, B. J., Flaud, J.-M., Gamache, R. R., Hodges, J. T., Jacquemart, D., Perevalov, V. I., Perrin, A., Shine, K. P., Smith, M.-A. H., Tennyson, J., Toon, G. C., Tran, H., Tyuterev, V. G., Barbe, A., Császár, A. G., Devi, V. M., Furtenbacher, T., Harrison, J. J., Hartmann, J.-M., Jolly, A., Johnson, T. J., Karman, T., Kleiner, I., Kyuberis, A. A., Loos, J., Lyulin, O. M., Massie, S. T., Mikhailenko, S. N., Moazzen-Ahmadi, N., Müller, H. S. P., Naumenko, O. V., Nikitin, A. V., Polyansky, O. L., Rey, M., Rotger, M., Sharpe, S. W., Sung, K., Starikova, E., Tashkun, S. A., Auwera, J. V., Wagner, G., Wilzewski, J., Wcisło, P., Yu, S., and Zak, E. J.: The HITRAN2016 molecular spectroscopic database, J. Quant. Spectrosc. Ra., 203, 3–69, https://doi.org/10.1016/j.jqsrt.2017.06.038, 2017.
Han, Y., van Delst, P., Liu, Q., Weng, F., Yan, B., Treadon, R., and Derber, J.: JCSDA Community Radiative Transfer Model (CRTM): version 1, NOAA technical report NESDIS, 122, https://repository.library.noaa.gov/view/noaa/1157 (last access: 29 February 2024), 2006.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hocking, J., Vidot, J., Brunel, P., Roquet, P., Silveira, B., Turner, E., and Lupu, C.: A new gas absorption optical depth parameterisation for RTTOV version 13, Geosci. Model Dev., 14, 2899–2915, https://doi.org/10.5194/gmd-14-2899-2021, 2021.
Hogan, R. J., Honeyager, R., Tyynelä, J., and Kneifel, S.: Calculating the Millimetre-Wave Scattering Phase Function of Snowflakes Using the Self-Similar Rayleigh–Gans Approximation, Q. J. Roy. Meteor. Soc., 143, 834–844, https://doi.org/10.1002/qj.2968, 2017.
Holdridge, D.: Balloon-Borne Sounding System (SONDE) Instrument Handbook, Atmospheric Radiation Measurement user facility, Pacific Northwest National Laboratory, Richland, WA, https://doi.org/10.2172/1020712, 2020.
Huang, Y. and Bani Shahabadi, M.: Why logarithmic? A note on the dependence of radiative forcing on gas concentration, J. Geophys. Res.-Atmos., 119, 13683–13689, https://doi.org/10.1002/2014JD022466, 2014.
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2391 pp., https://doi.org/10.1017/9781009157896, 2021.
ITU-R Recommendation P.835-6: Reference standard atmospheres, https://www.itu.int/rec/R-REC-P.835/en (last access: 26 July 2021), 2017.
Karpowicz, B. M., Stegmann, P. G., Johnson, B. T., Christophersen, H. W., Hyer, E. J., Lambert, A., and Simon, E.: pyCRTM: A python interface for the community radiative transfer model, J. Quant. Spectrosc. Ra., 288, 108263, https://doi.org/10.1016/j.jqsrt.2022.108263, 2022.
Keeler, E. and Burk, K.: Balloon-Borne Sounding System (SONDEWNPN), Atmospheric Radiation Measurement (ARM) program, https://doi.org/10.5439/1595321, 2012.
Koshelev, M. A., Golubiatnikov, G. Y., Vilkov, I. N., and Tretyakov, M. Y.: Line shape parameters of the 22-GHz water line for accurate modeling in atmospheric applications, J. Quant. Spectrosc. Ra., 205, 51–58, https://doi.org/10.1016/j.jqsrt.2017.09.032, 2018.
Koshelev, M. A., Vilkov, I. N., Makarov, D. S., Tretyakov, M. Y., Vispoel, B., Gamache, R. R., Cimini, D., Romano, F., and Rosenkranz, P. W.: Water vapor line profile at 183-GHz: Temperature dependence of broadening, shifting, and speed-dependent shape parameters, J. Quant. Spectrosc. Ra., 262, 107472, https://doi.org/10.1016/j.jqsrt.2020.107472, 2021.
Koshelev, M. A., Golubiatnikov, G. Y., Vilkov, I. N., and Tretyakov, M. Y.: Molecular oxygen fine structure with sub-kHz accuracy, J. Quant. Spectrosc. Ra., 278, 108001, https://doi.org/10.1016/j.jqsrt.2021.108001, 2022.
Larosa, S., Cimini, D., Gallucci, D., Di Paola, F., Nilo, S. T., Ricciardelli, E., Ripepi, E., and Romano, F.: A Cloud Detection Neural Network Approach for the Next Generation Microwave Sounder Aboard EPS MetOp-SG A1, Remote Sens.-Basel, 15, 1798, https://doi.org/10.3390/rs15071798, 2023.
Larosa, S., Cimini, D., Gallucci, D., Nilo, S. T., and Romano, F.: PyRTlib: a python package for non-scattering line-by-line mi-crowave Radiative Transfer simulations, (v1.0.5), Zenodo [software], https://doi.org/10.5281/zenodo.10729195, 2024a.
Larosa, S., Cimini, D., Gallucci, D., Nilo, S. T., and Romano, F.: PyRTlib: Example gallery, GitHub [tutorial], https://satclop.github.io/pyrtlib/en/main/examples/index.html, last access: 29 February 2024b.
Liebe, H. J.: MPM–An atmospheric millimeter-wave propagation model, Int. J. Infrared Milli., 10, 631–650, https://doi.org/10.1007/BF01009565, 1989.
Liebe, H. J., Hufford, G. A., and Manabe, T.: A model for the complex permittivity of water at frequencies below 1 THz, Int. J. Infrared Milli., 12, 659–675, https://doi.org/10.1007/BF01008897, 1991.
Liebe, H. J., Hufford, G. A., and Cotton, M.: Propagation modeling of moist air and suspended water/ice particles at frequencies below 1000 GHz, in AGARD, https://its.ntia.gov/publications/details.aspx?pub=2670 (last access: 29 February 2024), 1993.
Liljegren, J. C., Boukabara, S.-A., Cady-Pereira, K., and Clough, S. A.: The effect of the half-width of the 22-GHz water vapor line on retrievals of temperature and water vapor profiles with a 12-channel microwave radiometer, IEEE T. Geosci. Remote, 43, 1102–1108, https://doi.org/10.1109/TGRS.2004.839593, 2005.
Löhnert, U. and Maier, O.: Operational profiling of temperature using ground-based microwave radiometry at Payerne: prospects and challenges, Atmos. Meas. Tech., 5, 1121–1134, https://doi.org/10.5194/amt-5-1121-2012, 2012.
Makarov, D. S., Tretyakov, M. Y., and Rosenkranz, P. W.: Revision of the 60-GHz atmospheric oxygen absorption band models for practical use, J. Quant. Spectrosc. Ra., 243, 106798, https://doi.org/10.1016/j.jqsrt.2019.106798, 2020.
Martinet, P., Cimini, D., De Angelis, F., Canut, G., Unger, V., Guillot, R., Tzanos, D., and Paci, A.: Combining ground-based microwave radiometer and the AROME convective scale model through 1DVAR retrievals in complex terrain: an Alpine valley case study, Atmos. Meas. Tech., 10, 3385–3402, https://doi.org/10.5194/amt-10-3385-2017, 2017.
Martinet, P., Cimini, D., Burnet, F., Ménétrier, B., Michel, Y., and Unger, V.: Improvement of numerical weather prediction model analysis during fog conditions through the assimilation of ground-based microwave radiometer observations: a 1D-Var study, Atmos. Meas. Tech., 13, 6593–6611, https://doi.org/10.5194/amt-13-6593-2020, 2020.
Marzano, F. S., Mugnai, A., Panegrossi, G., Pierdicca, N., Smith, E. A., and Turk, J.: Bayesian estimation of precipitating cloud parameters from combined measurements of spaceborne microwave radiometer and radar, IEEE T. Geosci. Remote, 37, 596–613, https://doi.org/10.1109/36.739124, 1999.
Matricardi, M.: The generation of RTTOV regression coefficients for IASI and AIRS using a new profile training set and a new line-by-line database, ECMWF Technical Memoranda, 47, https://doi.org/10.21957/59u3oc9es, 2008.
Mech, M., Maahn, M., Kneifel, S., Ori, D., Orlandi, E., Kollias, P., Schemann, V., and Crewell, S.: PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere, Geosci. Model Dev., 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, 2020.
Mlawer, E. J., Payne, V. H., Moncet, J.-L., Delamere, J. S., Alvarado, M. J., and Tobin, D. C.: Development and recent evaluation of the MT_CKD model of continuum absorption, Philos. T. Roy. Soc. A, 370, 2520–2556, https://doi.org/10.1098/rsta.2011.0295, 2012.
Mollière, P., Wardenier, J. P., Boekel, R. van, Henning, T., Molaverdikhani, K., and Snellen, I. A. G.: petitRADTRANS – A Python radiative transfer package for exoplanet characterization and retrieval, A&A, 627, A67, https://doi.org/10.1051/0004-6361/201935470, 2019.
Moradi, I., Ferraro, R. R., Eriksson, P., and Weng, F.: Intercalibration and Validation of Observations From ATMS and SAPHIR Microwave Sounders, IEEE T. Geosci. Remote, 53, 5915–5925, https://doi.org/10.1109/TGRS.2015.2427165, 2015.
Payne, V. H., Delamere, J. S., Cady-Pereira, K. E., Gamache, R. R., Moncet, J.-L., Mlawer, E. J., and Clough, S. A.: Air-Broadened Half-Widths of the 22- and 183-GHz Water-Vapor Lines, IEEE T. Geosci. Remote, 46, 3601–3617, https://doi.org/10.1109/TGRS.2008.2002435, 2008.
Petty, G. W.: A First Course in Atmospheric Radiation, Sundog Publishing, Madison, WI, 2006.
Picard, G., Brucker, L., Fily, M., Gallée, H., and Krinner, G.: Modeling time series of microwave brightness temperature in Antarctica, J. Glaciol., 55, 537–551, https://doi.org/10.3189/002214309788816678, 2009.
Picard, G., Sandells, M., and Löwe, H.: SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0), Geosci. Model Dev., 11, 2763–2788, https://doi.org/10.5194/gmd-11-2763-2018, 2018.
Rosenkranz, P. W.: Absorption of microwaves by atmospheric gases, Chap. 2, Atmospheric Remote Sensing by Microwave Radiometry, edited by: Janssen, M. A., John Wiley and Sons, New York, http://hdl.handle.net/1721.1/68611, 1993.
Rosenkranz, P.: Line-by-line microwave radiative transfer (non-scattering), MWRnet – An International Network of Ground-based Microwave Radiometers [software], http://cetemps.aquila.infn.it/mwrnet/lblmrt_ns.html (last access: 29 February 2024), 2017.
Rosenkranz, P. W.: Interference coefficients for overlapping oxygen lines in air, J. Quant. Spectrosc. Ra., 39, 287–297, https://doi.org/10.1016/0022-4073(88)90004-0, 1988.
Rosenkranz, P. W.: Water vapor microwave continuum absorption: A comparison of measurements and models, Radio Sci., 33, 919–928, https://doi.org/10.1029/98RS01182, 1998.
Rosenkranz, P. W.: Retrieval of temperature and moisture profiles from AMSU-A and AMSU-B measurements, IEEE T. Geosci. Remote, 39, 2429–2435, https://doi.org/10.1109/36.964979, 2001.
Rosenkranz, P. W.: Comment on “Uncertainties in the temperature dependence of the line-coupling parameters of the microwave oxygen band: impact study,” IEEE T. Geosci. Remote, 43, 2160–2161, https://doi.org/10.1109/TGRS.2005.853189, 2005.
Rosenkranz, P. W.: A Model for the Complex Dielectric Constant of Supercooled Liquid Water at Microwave Frequencies, IEEE T. Geosci. Remote, 53, 1387–1393, https://doi.org/10.1109/TGRS.2014.2339015, 2015.
Rosenkranz, P. W. and Cimini, D.: Speed Dependence of 22- and 118-GHz Line Shapes for Tropospheric Remote Sensing, IEEE T. Geosci. Remote, 57, 9702–9708, https://doi.org/10.1109/TGRS.2019.2928570, 2019.
Rosenkranz, P. W., Cimini, D., Koshelev, M. A., and Tretyakov, M. Y.: Covariances of Spectroscopic Parameter Uncertainties in Microwave Forward Models and Consequences for Remote Sensing, in: 2018 IEEE 15th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad), 27–30 March 2018, Massachusetts Institute of Technology, United States, 1–6, https://doi.org/10.1109/MICRORAD.2018.8430729, 2018.
Rothman, L. S., Jacquemart, D., Barbe, A., Chris Benner, D., Birk, M., Brown, L. R., Carleer, M. R., Chackerian, C., Chance, K., Coudert, L. H., Dana, V., Devi, V. M., Flaud, J.-M., Gamache, R. R., Goldman, A., Hartmann, J.-M., Jucks, K. W., Maki, A. G., Mandin, J.-Y., Massie, S. T., Orphal, J., Perrin, A., Rinsland, C. P., Smith, M. A. H., Tennyson, J., Tolchenov, R. N., Toth, R. A., Vander Auwera, J., Varanasi, P., and Wagner, G.: The HITRAN 2004 molecular spectroscopic database, J. Quant. Spectrosc. Ra., 96, 139–204, https://doi.org/10.1016/j.jqsrt.2004.10.008, 2005.
Rüfenacht, R., Haefele, A., Pospichal, B., Cimini, D., Bircher-Adrot, S., Turp, M., and Sugier, J.: EUMETNET opens to microwave radiometers for operational thermodynamical profiling in Europe, Bull. Atmos. Sci. Technol., 2, 4, https://doi.org/10.1007/s42865-021-00033-w, 2021.
Saastamoinen, J.: Atmospheric Correction for the Troposphere and Stratosphere in Radioranging of Satellites, in the Use of Artificial Satellites for Geodesy, Geophys. Monogr. Ser., 15, 247–251, 1972.
Sanò, P., Panegrossi, G., Casella, D., Di Paola, F., Milani, L., Mugnai, A., Petracca, M., and Dietrich, S.: The Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for AMSU/MHS observations: description and application to European case studies, Atmos. Meas. Tech., 8, 837–857, https://doi.org/10.5194/amt-8-837-2015, 2015.
Saunders, R., Hocking, J., Turner, E., Rayer, P., Rundle, D., Brunel, P., Vidot, J., Roquet, P., Matricardi, M., Geer, A., Bormann, N., and Lupu, C.: An update on the RTTOV fast radiative transfer model (currently at version 12), Geosci. Model Dev., 11, 2717–2737, https://doi.org/10.5194/gmd-11-2717-2018, 2018.
Schreier, F., Gimeno García, S., Hedelt, P., Hess, M., Mendrok, J., Vasquez, M., and Xu, J.: GARLIC – A general purpose atmospheric radiative transfer line-by-line infrared-microwave code: Implementation and evaluation, J. Quant. Spectrosc. Ra., 137, 29–50, https://doi.org/10.1016/j.jqsrt.2013.11.018, 2014.
Schreier, F.: Computational aspects of speed-dependent Voigt profiles, J. Quant. Spectrosc. Ra., 187, 44-53, https://doi.org/10.1016/j.jqsrt.2016.08.009, 2017.
Schreier, F. and Hochstaffl, P.: Computational aspects of speed-dependent Voigt and Rautian profiles, J. Quant. Spectrosc. Ra., 258, 107385, https://doi.org/10.1016/j.jqsrt.2020.107385, 2021.
Schreier, F., Gimeno García, S., Hochstaffl, P., and Städt, S.: Py4CAtS–PYthon for Computational ATmospheric Spectroscopy, Atmosphere, 10, 262, https://doi.org/10.3390/atmos10050262, 2019.
Schroeder, J. A. and Westwater, E. R.: Users' guide to WPL microwave radiative transfer software, NOAA technical memorandum ERL WPL, 213, https://repository.library.noaa.gov/view/noaa/32511 (last access: 29 February 2024), 1991.
Shrestha, B., Brotzge, J. A., and Wang, J.: Evaluation of the New York State Mesonet Profiler Network data, Atmos. Meas. Tech., 15, 6011–6033, https://doi.org/10.5194/amt-15-6011-2022, 2022.
Titos, G., Jefferson, A., Sheridan, P. J., Andrews, E., Lyamani, H., Alados-Arboledas, L., and Ogren, J. A.: Aerosol light-scattering enhancement due to water uptake during the TCAP campaign, Atmos. Chem. Phys., 14, 7031–7043, https://doi.org/10.5194/acp-14-7031-2014, 2014.
Tretyakov, M. Y.: Spectroscopy underlying microwave remote sensing of atmospheric water vapor, J. Mol. Spectrosc., 328, 7–26, https://doi.org/10.1016/j.jms.2016.06.006, 2016.
Tretyakov, M. Y., Parshin, V. V., Koshelev, M. A., Shanin, V. N., Myasnikova, S. E., and Krupnov, A. F.: Studies of 183 GHz water line: broadening and shifting by air, N2 and O2 and integral intensity measurements, J. Mol. Spectrosc., 218, 239–245, https://doi.org/10.1016/S0022-2852(02)00084-X, 2003.
Tretyakov, M. Y., Koshelev, M. A., Dorovskikh, V. V., Makarov, D. S., and Rosenkranz, P. W.: 60-GHz oxygen band: precise broadening and central frequencies of fine-structure lines, absolute absorption profile at atmospheric pressure, and revision of mixing coefficients, J. Mol. Spectrosc., 231, 1–14, https://doi.org/10.1016/j.jms.2004.11.011, 2005.
Tripoli, G. J., Medaglia, C. M., Dietrich, S., Mugnai, A., Panegrossi, G., Pinori, S., and Smith, E. A.: The 9–10 November 2001 Algerian Flood: A Numerical Study, B. Am. Meteor. Soc., 86, 1229–1235, 2005.
Tritsis, A., Yorke, H., and Tassis, K.: Python Radiative Transfer Emission code (pyrate): non-local thermodynamic equilibrium spectral lines simulations, Mon. Not. R. Astron. Soc., 478, 2056–2064, https://doi.org/10.1093/mnras/sty1152, 2018.
Turner, D. D. and Löhnert, U.: Ground-based temperature and humidity profiling: combining active and passive remote sensors, Atmos. Meas. Tech., 14, 3033–3048, https://doi.org/10.5194/amt-14-3033-2021, 2021.
Turner, D. D., Cadeddu, M. P., Lohnert, U., Crewell, S., and Vogelmann, A. M.: Modifications to the Water Vapor Continuum in the Microwave Suggested by Ground-Based 150-GHz Observations, IEEE T. Geosci. Remote, 47, 3326–3337, https://doi.org/10.1109/TGRS.2009.2022262, 2009.
Turner, E., Rayer, P., and Saunders, R.: AMSUTRAN: A microwave transmittance code for satellite remote sensing, J. Quant. Spectrosc. Ra., 227, 117–129, https://doi.org/10.1016/j.jqsrt.2019.02.013, 2019.
Vermote, E. F., Tanre, D., Deuze, J. L., Herman, M., and Morcette, J.-J.: Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview, IEEE T. Geosci. Remote, 35, 675–686, https://doi.org/10.1109/36.581987, 1997.
Wang, D., Prigent, C., Kilic, L., Fox, S., Harlow, C., Jimenez, C., Aires, F., Grassotti, C., and Karbou, F.: Surface Emissivity at Microwaves to Millimeter Waves over Polar Regions: Parameterization and Evaluation with Aircraft Experiments, J. Atmos. Ocean. Tech., 34, 1039–1059, https://doi.org/10.1175/JTECH-D-16-0188.1, 2017.
Wei, S.-W., Lu, C.-H., Johnson, B. T., Dang, C., Stegmann, P., Grogan, D., Ge, G., and Hu, M.: The Influence of Aerosols on Satellite Infrared Radiance Simulations and Jacobians: Numerical Experiments of CRTM and GSI, Remote Sens.-Basel, 14, 683, https://doi.org/10.3390/rs14030683, 2022.
Wilson, R. T.: Py6S: A Python interface to the 6S radiative transfer model, Comput. Geosci., 51, 166–171, https://doi.org/10.1016/j.cageo.2012.08.002, 2013.
Short summary
PyRTlib is an attractive educational tool because it provides a flexible and user-friendly way to broadly simulate how electromagnetic radiation travels through the atmosphere as it interacts with atmospheric constituents (such as gases, aerosols, and hydrometeors). PyRTlib is a so-called radiative transfer model; these are commonly used to simulate and understand remote sensing observations from ground-based, airborne, or satellite instruments.
PyRTlib is an attractive educational tool because it provides a flexible and user-friendly way...
